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or research conferences. Expertise in one or more of the above areas of interest. Experience with neuromorphic computing, IC design, embedded systems, or AI. Experience with research on Neuromorphic Computing
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large-language models (LLMs) are about to turn computer-aided engineering into true human–AI co-design. In the new MSCA Doctoral Network GenAIDE we team up with Honda Research Institute Europe, Altair
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? Generative AI and large-language models (LLMs) are about to turn computer-aided engineering into true human–AI co-design. In the new MSCA Doctoral Network GenAIDE we team up with Honda Research Institute
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journals or research conferences. Expertise in one or more of the above areas of interest. Experience with neuromorphic computing, IC design, embedded systems, or AI. Experience with research on Neuromorphic
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Why apply? Generative AI and large-language models (LLMs) are about to turn computer-aided engineering into true human–AI co-design. In the new MSCA Doctoral Network GenAIDE we team up with Honda
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the combined themes of human-computer interaction and critical computing. The lab will be exploring the notion of "deceptive by design" on all fronts: social identity cues in the design of LLM-based chatbots
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. Daniel Merkle. The overall research project is based on the novel application of formalisms, algorithms, and computational methods from computer science to the design of microbial communities, ultimately
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 10 hours ago
to research in computer science and mathematics. 400 R&D engineers support research scientists by developing software tools and platforms that help the research process and the experimentation. Spirals
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. Daniel Merkle. The overall research project is based on the novel application of formalisms, algorithms, and computational methods from computer science to the design of microbial communities, ultimately
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-training that facilitate few-shot adaptations. The research will show how the complementarity of event-triggered learning and meta-learning can drastically increase RL efficiency at test time. The co-design